In recent years, the ability to profile customer behavior and to conduct associated pattern analysis has become desirable. For example, the need for improved techniques in these areas has recently been identified in the fast-growing fields of telecommunications and electronic commerce, or e-commerce. However, existing tools and techniques do not deliver sufficient capabilities.
For example, one technique involves creating customer behavior profiles from very large collections of transaction data. More particularly, a database is formed with transaction data, the database is batch updated, and the database is structured for fast online queries and summaries by department managers. For example, in telecommunications applications, hundreds of millions of call records may have to be processed daily in order to create and incrementally update customer calling behavior profiles. These customer calling behavior profiles are contained within data warehouse systems. Hence, such data warehouses can contain enormous amounts of data which can make searching for one customer profile unwieldy and time consuming.
Therefore, there exists a present need for improved techniques for profiling customer behavior.